Abstract
This paper reports the Cooking Activity Recognition Challenge by team Rit’s cooking held at International Conference on Activity and Behavior Computing (ABC 2020). Our approach leverages the convolutional layer and LSTM to recognize macro activities (recipe), and micro activities (body motion). For micro activities consisting of multiple labels in a segment, loss is calculated using BCEWithLogistsLoss function in PyTorch for each body part, and then the final decision is made by majority vote of classification results for each body part.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bayat, A., Pomplun, M., Tran, D.A.: A study on human activity recognition using accelerometer data from smartphones 34, 450–457 (2014)
Wang, A., Chen, G., Yang, J., Zhao, S., Chang, C.: A comparative study on human activity recognition using inertial sensors in a smartphone 16(11), 4566–4578 (2016)
Javier Ordóñez, F., Roggen, D.: Deep, convolutional and lstm recurrent neural networks for multimodal wearable activity recognition 16, 1–25 (2016)
Atallah, L., Lo, B., King, R., Yang, G.: Sensor positioning for activity recognition using wearable accelerometers 5(4), 320–329 (2011)
Casale, P., Pujol, O., Radeva, P.: Human activity recognition from accelerometer data using a wearable device. In: Pattern Recognition and Image Analysis, pp. 289–296 (2011)
Lago, P., Takeda, S., Adachi, K., Shamma Alia, S., Matsuki, M., Benaissa, B., Inoue, S., Charpillet, F.: Cooking activity dataset with macro and micro activities (2020). https://doi.org/10.21227/hyzg-9m49
Lago, P., Takeda, S., Shamma Alia, S., Adachi, K., Benaissa, B., Charpillet, F., Inoue, S.: A dataset for complex activity recognition with micro and macro activities in a cooking scenario (2020)
Shamma Alia, S., Lago, P., Takeda, S., Adachi, K., Benaissa, B., Ahad, Md A.R., Inoue, S.: Summary of the cooking activity recognition challenge (2020)
Jiang, W., Yin, Z.: Human activity recognition using wearable sensors by deep convolutional neural networks. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 1307–1310 (2015)
Wang, W., Liu, A.X., Shahzad, M., Ling, K., Lu, S.: Device-free human activity recognition using commercial wifi devices 35(5), 1118–1131 (2017)
Chen, Y., Shen, C.: Performance analysis of smartphone-sensor behavior for human activity recognition 5, 3095–3110 (2017)
Chen, Y., Zhong, K., Zhang, J., Sun, Q., Zhao, X.: LSTM Networks for Mobile Human Activity Recognition. In: 2016 International Conference on Artificial Intelligence: Technologies and Applications, pp. 50–53 (2016)
Chen, Y., Xue, Y.: A deep learning approach to human activity recognition based on single accelerometer. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1488–1492 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Fujii, A., Kajiwara, D., Murao, K. (2021). Cooking Activity Recognition with Convolutional LSTM Using Multi-label Loss Function and Majority Vote. In: Ahad, M.A.R., Lago, P., Inoue, S. (eds) Human Activity Recognition Challenge. Smart Innovation, Systems and Technologies, vol 199. Springer, Singapore. https://doi.org/10.1007/978-981-15-8269-1_8
Download citation
DOI: https://doi.org/10.1007/978-981-15-8269-1_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8268-4
Online ISBN: 978-981-15-8269-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)